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Computer Aided Diagnosis Using Multilevel Image Features on Large-Scale Evaluation

机译：大规模评估中使用多级图像特征的计算机辅助诊断

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摘要

Computer aided diagnosis (CAD) of cancerous anatomical structures via 3D medical images has emerged as an intensively studied research area. In this paper, we present a principled three-tiered image feature learning approach to capture task specific and data-driven class discriminative statistics from an annotated image database. It integrates voxel-, instance-, and database-level feature learning, aggregation and parsing. The initial segmentation is proceeded as robust voxel labeling and thresholding. After instance-level spatial aggregation, extracted features can also be flexibly tuned for classifying lesions, or discriminating different subcategories of lesions. We demonstrate the effectiveness in the lung nodule detection task which handles all types of solid, partial-solid, and ground-glass nodules using the same set of learned features. Our hierarchical feature learning framework, which was extensively trained and validated on large-scale multiple site datasets of 879 CT volumes (510 training and 369 validation), achieves superior performance than other state-of-the-art CAD systems. The proposed method is also shown to be applicable for colonic polyp detection, including all polyp morphological subcategories, via 770 tagged-prep CT scans from multiple medical sites (358 training and 412 validation).
机译：通过3D医学图像对癌症解剖结构进行计算机辅助诊断（CAD）已成为一个深入研究的研究领域。在本文中，我们提出了一种有原则的三层图像特征学习方法，以从带注释的图像数据库中捕获任务特定的和数据驱动的类判别统计信息。它集成了体素，实例和数据库级的功能学习，聚合和解析。最初的分割是通过强大的体素标记和阈值化进行的。在实例级空间聚集之后，还可以灵活地调整提取的特征，以对病变进行分类或区分病变的不同子类别。我们证明了肺结节检测任务的有效性，该任务使用相同的学习功能来处理所有类型的实心，部分实心和毛玻璃结节。我们的分层特征学习框架在879个CT卷的大规模多站点数据集上进行了广泛的培训和验证（510个培训和369个验证），其性能优于其他最新的CAD系统。通过从多个医疗地点进行的770次带标签的CT扫描（358训练和412验证），所提出的方法也显示适用于结肠息肉检测，包括所有息肉形态学亚类。

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